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With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it’s still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.
Xiaohua Zhou; Xuezhi Wang; Yuanchun Zhou; Qinghui Lin; Jianghua Zhao; Xianghai Meng. RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System. Remote Sensing 2021, 13, 1815 .
AMA StyleXiaohua Zhou, Xuezhi Wang, Yuanchun Zhou, Qinghui Lin, Jianghua Zhao, Xianghai Meng. RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System. Remote Sensing. 2021; 13 (9):1815.
Chicago/Turabian StyleXiaohua Zhou; Xuezhi Wang; Yuanchun Zhou; Qinghui Lin; Jianghua Zhao; Xianghai Meng. 2021. "RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System." Remote Sensing 13, no. 9: 1815.
China is experiencing severe PM 2 . 5 (fine particles with a diameter of 2.5 μ g or smaller) pollution problem. Little is known, however, about how the increasing concentration trend is spatially distributed, nor whether there are some areas that experience a stable or decreasing concentration trend. Managers and policymakers require such information to make strategic decisions and monitor progress towards management objectives. Here, we present a pixel-based linear trend analysis of annual PM 2 . 5 concentration variation in China during the period 1999–2016, and our results provide guidance about where to prioritize management efforts and affirm the importance of controlling coal energy consumption. We show that 87.9% of the whole China area had an increasing trend. The drastic increasing trends of PM 2 . 5 concentration during the last 18 years in the Beijing–Tianjin–Hebei region, Shandong province, and the Three Northeastern Provinces are discussed. Furthermore, by exploring regional PM 2 . 5 pollution, we find that Tarim Basin endures a high PM 2 . 5 concentration, and this should have some relationship with oil exploration. The relationship between PM 2 . 5 pollution and energy consumption is also discussed. Not only energy structure reconstruction should be repeatedly emphasized, the amount of coal burned should be strictly controlled.
Jianghua Zhao; Xuezhi Wang; Hongqing Song; Yi Du; Wenjuan Cui; Yuanchun Zhou. Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016. Atmosphere 2019, 10, 461 .
AMA StyleJianghua Zhao, Xuezhi Wang, Hongqing Song, Yi Du, Wenjuan Cui, Yuanchun Zhou. Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016. Atmosphere. 2019; 10 (8):461.
Chicago/Turabian StyleJianghua Zhao; Xuezhi Wang; Hongqing Song; Yi Du; Wenjuan Cui; Yuanchun Zhou. 2019. "Spatiotemporal Trend Analysis of PM2.5 Concentration in China, 1999–2016." Atmosphere 10, no. 8: 461.
Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.
Jianhua Wang; Qiming Qin; Zhongling Gao; Jianghua Zhao; Xin Ye. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS International Journal of Geo-Information 2016, 5, 114 .
AMA StyleJianhua Wang, Qiming Qin, Zhongling Gao, Jianghua Zhao, Xin Ye. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS International Journal of Geo-Information. 2016; 5 (7):114.
Chicago/Turabian StyleJianhua Wang; Qiming Qin; Zhongling Gao; Jianghua Zhao; Xin Ye. 2016. "A New Approach to Urban Road Extraction Using High-Resolution Aerial Image." ISPRS International Journal of Geo-Information 5, no. 7: 114.
With the emerging of vast quantities of geospatial data, large temporal and spatial scale of data are used in geosciences research nowadays. As a lot of data processing tasks such as image interpretation are hard to be processed automatically, and the data process workload is huge, crowdsourcing is studied as a supplement tool of cloud computing technology and advanced algorithms. This paper outlines the procedure and methodology of applying crowdsourcing in geoscientific data process. And based on the GSCloud platform, a case study of Qinghai-Tibetan Lake Extraction task has been carried out to explore the feasibility of the application of crowdsourcing in geosciences. By analyzing the case, the paper summarizes the problems and characteristics, and advantages and challenges are also presented at last.
Jianghua Zhao; Xuezhi Wang; Qinghui Lin; Jianhui Li. Exploration of Applying Crowdsourcing in Geosciences: A Case Study of Qinghai-Tibetan Lake Extraction. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016, 329 -334.
AMA StyleJianghua Zhao, Xuezhi Wang, Qinghui Lin, Jianhui Li. Exploration of Applying Crowdsourcing in Geosciences: A Case Study of Qinghai-Tibetan Lake Extraction. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2016; ():329-334.
Chicago/Turabian StyleJianghua Zhao; Xuezhi Wang; Qinghui Lin; Jianhui Li. 2016. "Exploration of Applying Crowdsourcing in Geosciences: A Case Study of Qinghai-Tibetan Lake Extraction." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 329-334.
Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection and assessment solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, and aspect ratio are selected to form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads are detected by applying the knowledge model. In order to quantitatively assess the damage degree, damage assessment indicators with their corresponding standard of damage grade are also proposed. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake on 15 May 2008. The results show that the producer’s accuracy (PA) and user’s accuracy (UA) reached about 90% and 85%, respectively, indicating that the proposed method is effective for road damage detection and assessment. This approach also significantly reduces the need for pre-disaster remote sensing data.
Jianhua Wang; Qiming Qin; Jianghua Zhao; Xin Ye; Xiao Feng; Xuebin Qin; Xiucheng Yang. Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image. Remote Sensing 2015, 7, 4948 -4967.
AMA StyleJianhua Wang, Qiming Qin, Jianghua Zhao, Xin Ye, Xiao Feng, Xuebin Qin, Xiucheng Yang. Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image. Remote Sensing. 2015; 7 (4):4948-4967.
Chicago/Turabian StyleJianhua Wang; Qiming Qin; Jianghua Zhao; Xin Ye; Xiao Feng; Xuebin Qin; Xiucheng Yang. 2015. "Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image." Remote Sensing 7, no. 4: 4948-4967.